Agent-Based Models of Supply Chains

  • B. Behdani
  • K. H. van Dam
  • Z. Lukszo
Part of the Agent-Based Social Systems book series (ABSS, volume 9)


Based on the modelling steps discussed in Part I, this chapter aims to present ways in which agent-based simulation models of supply chains can be developed and used to improve the performance of these systems in both normal and abnormal situations. An industrial supply chain with a network of several independent companies is a good example of a socio-technical system. The physical and social networks of the actors involved in their operation collectively form an interconnected, complex system in which the actors determine the development and operation of the physical network and, likewise, the physical network affects the behaviour of the actors. In this type of system, the many interactions taking place in the social and physical subsystems can result in the complex, dynamic behaviour of the supply chain as a whole. Accordingly, any attempt to improve the functioning of the supply chain requires a comprehensive understanding of this behaviour under different supply network configurations. Most of the current approaches to the modelling and simulation of supply chains do not capture the rich socio-technical dynamics present. The agent-based modelling approach, however, seems to be very promising as a means to address this complex behaviour. To demonstrate its applicability, we will present agent-based simulation models for two different industrial supply chains: an oil refinery and a multi-plant chemical enterprise. Using the models described in this chapter, the outcomes of the system under a broad range of possible agent behavioural rules and environmental events can be explored, and improved levels of system functioning can be identified.


Supply Chain Supply Chain Management Customer Order Total Tardiness Reorder Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to express their thanks to Rajagopalan Srinivasan and Arief Adhitya for their support in the development of the models used in both case studies.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Delftthe Netherlands

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